Highlights
• Tests association between generalized authoritarianism and subjective well-being.
• Generalized authoritarianism is positively associated with subjective well-being.
• This relation was significant independent of basic personality dimensions.
• Suggests that authoritarianism may be “bad” for others but “good” for the self.

Abstract

Although authoritarianism can negatively impact others (e.g., by predicting prejudiced intergroup attitudes), implications for the self are mixed and require clarification. Extending previous research, we examined the association between generalized authoritarianism (GA, indicated by right-wing authoritarianism and social dominance orientation) and subjective well-being (SWB, indicated by positive affect, negative affect, and life satisfaction) by testing simultaneously the general-level association between GA and SWB as well as specific residual associations between GA and SWB components, independent of basic personality dimensions. We observed a significant general-level association between GA and SWB whereby heightened authoritarianism predicted greater SWB. No residual associations were found between specific GA and SWB components. Despite being “bad” for others, generalized authoritarianism may be “good” for the self.

In a pair of seminal papers, Sewall Wright and Gustave Malécot introduced FST as a measure of structure in natural populations. In the decades that followed, a number of papers provided differing definitions, estimation methods, and interpretations beyond Wright's. While this diversity in methods has enabled many studies in genetics, it has also introduced confusion about how to estimate FST from available data. Considering this confusion, wide variation in published estimates of FST for pairs of HapMap populations is a cause for concern. These estimates changed- in some cases more than two-fold- when comparing estimates from genotyping arrays to those from sequence data (1000 Genomes Project Consortium 2010; International HapMap 3 Consortium 2010). Indeed, changes in FST from sequencing data might be expected data due to population genetic factors affecting rare variants. While rare variants do influence the result, we show that this is largely through differences in estimation methods. Correcting for this yields estimates of FST that are much more concordant between sequence and genotype data. These differences relate to three specific issues: (1) estimating FST for a single SNP, (2) combining estimates of FST across multiple SNPs, and (3) selecting the set of SNPs used in the computation. Changes in each of these aspects of estimation may result in FST estimates that are highly divergent from one another. Here, we clarify these issues and propose solutions.

As the title indicates, our paper is about the problem of inferring an “ancestral recombination graph,” or ARG, from sequence data. This is a topic that may strike many readers as impenetrably obscure and technical, so I will first try to explain, in plain language, what the ARG describes and why it has so much potential to be useful in many kinds of genetic analysis. Then, I will tell the story of how I and members of my research group have become increasingly fascinated by this problem over the years, how we have struggled with it, and how we finally achieved the conceptual breakthrough that is described in our paper. As will become evident, Matt Rasmussen, a former postdoc in the group and lead author of our paper, was central in this achievement.

What is the ARG?

The ARG is an elegantly simple yet superbly rich data structure that describes the complete evolutionary history of a collection of genetic sequences drawn from individuals in one or more populations. It was invented in the mid 1990s by the mathematicians Bob Griffiths and Paul Marjoram. The ARG captures essentially all evolutionary information relevant for genetic analysis of such sequences. Statisticians say that it fully defines the “correlation structure” of the sequences, meaning that it explains most similarities and differences among the sequences in terms of their patterns of shared ancestry.

The ARG is something like a family tree, only richer, because it not only defines the relationships among individuals, but it also traces the histories of specific segments of DNA sequences. For example, if you were to replace your family tree with an ARG, you could tell exactly which pieces of your genome came from your eccentric great grandmother and which pieces you share with your charming, intelligent, and handsome third cousin. [. . .]

The significance of recombinations and coalescences comes from the fact that these are the two ways in which lineages can join or split over time. The best way to understand them is to think about the behavior of lineages as one looks backward in time. The graph is typically laid out with time on the vertical axis, so that the bottom of the graph represents the present time and each node is assigned a height above this baseline indicating the time before the present at which the associated event occurred. Therefore, to look backward in time, we look upward in the graph. As we do so, we see that recombination events cause a single lineage to split into two ancestral lineages (representing the two sequence fragments that were joined together by the recombination in forward time), and coalescence events cause two lineages to join into one. Therefore, recombination nodes have one edge coming in and two going out, and coalescence nodes have two edges coming in and one going out. One way of thinking about it is that, given a fragment of modern DNA, recombinations have the effect of increasing its set of ancestors, while coalescences have the effect of decreasing its set of ancestors. [. . .]

Demographic Inference and Whole Genome Scan for Positive Natural Selection in
Pygmies from Central Africa
PingHsun Hsieh1, Krishna R. Veeramah1, Joseph Lachance2, Sarah A. Tishkoff2, Jeff D. Wall3,
Michael F. Hammer1, Ryan N. Gutenkunst1
1University of Arizona; 2University of Pennsylvania; 3University of California, San Francisco
African Pygmies are hunter-gatherers residing mostly in Central African rainforests. Many
Pygmy populations have been influenced by neighboring Niger-Kordofanian speaking farmer
populations through socio-economic contacts, particularly since the extensive agriculture
expansion in sub-Saharan Africa beginning five thousand years ago (kya). This complex
demographic history must be controlled in order to find true signatures of adaptation to the high
temperature, high humidity, and pathogen and parasite-enriched rainforest habitat of pygmies.
We sequenced and obtained whole-genome sequences at >40X coverage for Baka pygmies
from Cameroon, Biaka pygmies from the Central African Republic, and Niger-Kordofanian
speaking Yoruba farmers from Nigeria. We used the model-based demographic inference tool
∂a∂i to infer the history of these populations. Our best-fit model suggests that the farmer and
pygmy ancestors diverged from each other 150 kya and remained isolated from each other until
40 kya. This divergence is more ancient than estimated by previous studies using fewer loci, but
is confirmed using PSMC, another demographic inference tool that uses different genomic
information from ∂a∂i. Interestingly, our analysis shows that models with bi-directional
asymmetric gene flow between farmers and pygmies are statistically better supported than
previously suggested models with a single wave of uni-directional migration from farmers to
pygmies. To identify possible targets of adaptation, we conducted a genomic scan using
complementary methods, including the frequency-spectrum based G2D test, the population
differentiation based XP-CLR test, and the haplotype based iHS test. We performed 10,000
simulations based on the above best-fit demographic model in order to assign the significance
to each reported target of natural selection. Preliminary results reveal that genes involved in cell
adhesion, cellular signaling, olfactory perception, and immunity were likely targeted by natural
selection in the pygmies or their recent ancestors.

Whole genome sequencing and SNP genotyping arrays can paint strikingly different pictures of demographic history and natural selection. This is because genotyping arrays contain biased sets of pre-ascertained SNPs. In this short review, we use comparisons between high-coverage whole genome sequences of African hunter-gatherers and data from genotyping arrays to highlight how SNP ascertainment bias distorts population genetic inferences. Sample sizes and the populations in which SNPs are discovered affect the characteristics of observed variants. We find that SNPs on genotyping arrays tend to be older and present in multiple populations. In addition, genotyping arrays cause allele frequency distributions to be shifted towards intermediate frequency alleles, and estimates of linkage disequilibrium are modified. Since population genetic analyses depend on allele frequencies, it is imperative that researchers are aware of the effects of SNP ascertainment bias. With this in mind, we describe multiple ways to correct for SNP ascertainment bias.

Many of the mutations are detrimental to fitness, and each individual carries a burden of
deleterious mutations that were accumulated over many generations. In humans, the number of
de novo point mutations passed on to an offspring is strongly dependent on the father’s age.
Here, we use extensive pedigree data on a pre-industrial Finnish population to get, for each
individual, the ages of his or her male ascendants for up to three generations, and use this data
as a proxy for the number of acquired mutations. Individuals whose fathers, grandfathers and
great-grandfathers fathered their lineage at age of 20 were ~9% more likely to survive to
adulthood than those with 40-year-old male ancestors. Among survivors to adulthood, older
male ascendants were also associated with a reduced probability of getting married. These
observations suggest that the deleterious mutations acquired from recent ancestors may be a
substantial burden to fitness in humans.

Analysis of archaic genomes has documented 1-4% gene flow from Neandertals into the
ancestors of all non-Africans. As a first step to understanding the phenotypic impact of
introgressed regions, we built a map of Neandertal ancestry in modern humans, using data from
all populations in Phase 1 of the 1000 genomes project, combined with a high coverage (50×)
Neandertal genome.

• We identified Neandertal alleles that are at higher frequency than expected under a model of
neutral evolution, and identify dozens of genomic locations in Europeans and East Asians at
which the Neandertal alleles are the targets of positive selection. Interestingly, there is evidence
for more extensive positive selection in East Asian than in European populations.

• We discovered many more large genomic regions that are deficient in Neandertal ancestry
than expected by chance. These regions are about 10 Mb on average and about half are shared
between the European and East Asian populations. These observations are consistent with a
model in which these regions harbor hybrid incompatibility loci where Neandertal variants that
introgressed into modern humans were rapidly selected away.

• There is variation in the Neandertal ancestry across chromosomes, with chromosome X being
a desert (a third of the average) that contains only a few oases of Neandertal ancestry. This
further supports the hypothesis of genetic incompatibility between Neandertals and modern
humans, as hybrid incompatibility loci are known to concentrate on chromosome X.

• By piecing together the segments of confidently inferred Neandertal ancestry, we create a
tiling path covering about 40% of the genome that allows us to infer Neandertal ancestry even at
repetitive elements. We combine this with direct Neandertal sequence data to obtain a more
complete Neandertal reference genome sequence.